Although there are 8117 possible actions in the game, in some context, the decisions to be taken are straightforward.
To this end, it is better to script the behaviour rather than let the bot to predict an action.
As an example, during a block roll, i.e., the attack performed against an enemy piece, the result to be chosen is often clear (always put the enemy to the ground while not incurring in a turnover).
At the same time, removing actions from those available, allows to influence the behaviour of the bot, e.g., by focusing on safer plays or to encode some desired behaviour.
This design choice is known as action-space shaping [1]} and it is widespread in reinforcement learning bots deployed in gaming environments.
